1. Assignment of PROSITE motifs to topological regions: Application to a novel database of well characterised transmembrane proteins Tim Nugent

2. Identification of transmembrane regions To generate data for a plot, the protein sequence is scanned with a moving window of size 19-21 residues. At each position, the mean hydrophobic index of the amino acids within the window is calculated and that value plotted as the midpoint of the window. Aquaporin

3. The Positive Inside Rule Hydrophobic: Val, Phe, Ile, Leu, Met. Positive: Lys, Arg, His. Cytoplasmic loops are enriched in positively charged residues: the 'positive-inside rule' of von Heijne Ammonia Channel Cytoplasmic Extracellular

4. Topology prediction Previous methods based on the physiochemical principle of a sliding window of hydrophobicity have been replaced by machine learning approaches. These include hidden Markov models (PHOBIUS) and neural networks (MEMSAT3), which prevail over hydrophobicity methods due to their probabilistic orientation. MEMSAT3, which makes use of evolutionary information via PSI-BLAST profiles to enhance the prediction, is currently the most successful method with 80% success on the Möller dataset. The challenge now is to develop new tools to build on MEMSAT3 with the expectation that accuracy will surpass this threshold. Assembly of a new data set. Assignement of motifs to highlight topogenic bias.

5. Assembling a novel data set of transmembrane proteins In order to study and predict features of transmembrane proteins, the use of a high quality data set containing sequences with experimentally confirmed TM regions is essential. The data set was based on the widely used Möller test set (2001). Additional data was collected from MPTOPO, OPM, SWISSPROT and from the literature. Sequences were blasted against the PDB in order to identify entries for which the TM region had complete structural coverage. This set was then homology reduced at the 40% sequence identity level. The final data set contains 141 sequences, all with available structures, verifiable topology and N-terminal locations. 111 Alpha-helical proteins 30 Beta-barrel proteins

6. MySQL table schema

7. Assignment of PROSITE motifs to topological regions We next explored the possibility that motifs from the PROSITE database could be used as constraints in subsequent topology prediction steps, by identifying a bias in their inside/outside frequency. Extracelullar Cytoplasm

8. Alpha-helical protein PROSITE motif assignments

9. A Bioperl module to draw transmembrane proteins

10. Conclusions Created a high quality dataset of transmembrane proteins of known topology. Scanned the novel data set against motif and domain databases to identify signatures which were consistently located on either inside or outside loops. In collaboration with Dr Sara Mole (MRC Laboratory for Molecular Cell Biology), I have begun an analysis of CLN3 (Batten's Disease protein) with a view to predicting the protein's topology using a combination of computational and experimental evidence. Future work Determine if PROSITE motif bias can be used to improve topology prediction Amino acid topogenic propensities Module detection Lipid exposure Re-entrant regions Signal Peptides